Functional genomics builds naturally on recent successes in comparative prokaryotic genomics. The power of these methods for interrogation of the pathways regulating growth has recently been demonstrated for yeast, but the field is less developed for bacteria, partly due to a lack of experimental tools. We propose to develop and exploit quantitative genetic approaches for prokaroytic organisms, opening up the power of these approaches initially to two organisms, E. coli, a gram negative model organism and S. pneumoniae, a gram positive pathogen. We will build the methodology we developed in E. coli to systematically introduce gene disruptions two at a time. We will carry out the procedure en masse, initially focusing on genes involved in cell envelope function and DNA metabolism, so that the effect on bacterial growth of thousands of combinations of pair-wise disruptions can be analyzed and compared. We will apply the analytical tools that had led to important insights into yeast cell biology to our data set and refine them for bacteria. These approaches have proven powerful for discovering the function of uncharacterized genes and the nature of protein pathways and networks within the cell. We will complement quantitative genetic interaction studies with chemical genetic initiatives, thus experimentally linking pharmacological targets to the genes involved in their biology. In this way, a more complete picture of how bacterial proteins function, and how different areas of bacterial cell biology are interconnected, will be assembled. Moreover, this work in E. coli and S. pneumoniae will confer more power on existing comparative genomic data for hundreds of bacteria. Great emphasis will be placed on dissemination of the results (which will be of wide interest) via searchable database that will link to other relevant websites (e.g. EcoliHub) so that diverse datasets can be integrated, as well as providing the community with the experimental tools (strains, plasmids, libraries, computer programs etc.) needed to extend this approach to other organisms. Bacteria are among the simplest organisms in nature. By removing genes two at a time and observing the effect, we will build the first comprehensive picture of how the E.coli bacterium's 4000 genes relate to each other. This type of work can help us understand how a bacterial cell works, and the information can be used to design useful organisms for industry, to identify drug targets, and improve therapy for bacterial disease.